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Abstract Low back pain (LBP) is the leading cause of disability worldwide and often associated with lifestyle factors. However, studies further examining the role of these lifestyle factors in non-specific low back pain in comparison with radicular pain are sparse. The aim of this cross sectional study was to investigate how diverse lifestyle factors are associated with LBP. The study population of 3385 middle aged adults with and without low back pain was drawn from a large Birth 1966 Cohort. Outcome measures were steps per day, abdominal obesity, physical activity and endurance of the back muscles. Back static muscular endurance, abdominal obesity and physical activity were measured by means of the Biering–Sørensen test, waist circumference and a wrist worn accelerometer, respectively. Logistic regression analysis was applied to estimate associations of back static muscular endurance, abdominal obesity and accelerometer-measured physical activity with non-specific low back pain and radicular pain. An additional 1000 steps per day were associated with 4% lower odds of having non-specific low back pain. Participants with abdominal obesity had 46% higher odds of having radicular pain, whereas increases of 10 s in back static muscular endurance and 10 min in daily vigorous physical activity were associated with 5% and 7% lower odds of having radicular pain, respectively. In this population-based study, non-specific low back pain and radicular pain were associated with different lifestyle and physical factors at midlife. Non-specific low back pain was associated only with the average daily number of steps, whereas abdominal obesity was the strongest determinant of radicular pain, followed by vigorous physical activity and back static muscular endurance. The findings of this study contribute to better understand the role of lifestyle factors in both non-specific low back pain and radicular pain. Future longitudinal studies are required to explore causality.

AbstractObjectives Discriminating sleep period from accelerometer data remains a challenge despite many studies have adapted 24-h measurement protocols. We aimed to compare and examine the agreement among device-estimated and self-reported bedtime, wake-up time, and sleep periods in a sample of adults.Materials and methods Participants (108 adults, 61 females) with an average age of 33.1 (SD 0.4) were asked to wear two wearable devices (Polar Active and Ōura ring) simultaneously and record their bedtime and wake up time using a sleep diary. Sleep periods from Polar Active were detected using an in-lab algorithm, which is openly available. Sleep periods from Ōura ring were generated by commercial Ōura system. Scatter plots, Bland–Altman plots, and intraclass correlation coefficients (ICCs) were used to evaluate the agreement between the methods.Results Intraclass correlation coefficient values were above 0.81 for bedtimes and wake-up times between the three methods. In the estimation of sleep period, ICCs ranged from 0.67 (Polar Active vs. sleep diary) to 0.76 (Polar Active vs. Ōura ring). Average difference between Polar Active and Ōura ring was −1.8 min for bedtimes and −2.6 min for wake-up times. Corresponding values between Polar Active and sleep diary were −5.4 and −18.9 min, and between Ōura ring and sleep diary −3.6 min and −16.2 min, respectively.Conclusion Results showed a high agreement between Polar Active activity monitor and Ōura ring for sleep period estimation. There was a moderate agreement between self-report and the two devices in estimating bedtime and wake-up time. These findings suggest that potentially wearable devices can be interchangeably used to detect sleep period, but their accuracy remains limited.Key MessagesEstimation of sleep period from different devices could be comparable.Difference between sleep periods from monitors and sleep diary are under 20 min.Device-based estimation of sleep period is encouraged in population-based studies.

Abstract Background Articular surface size is traditionally considered to be a relatively stable trait throughout adulthood. Increased joint size reduces bone and cartilage tissue strains. Although physical activity (PA) has a clear association with diaphyseal morphology, the association between PA and articular surface size is yet to be confirmed. This cross-sectional study aimed to clarify the role of moderate-to-vigorous PA (MVPA) in knee morphology in terms of tibiofemoral joint size. Methods A sample of 1508 individuals from the population-based Northern Finland Birth Cohort 1966 was used. At the age of 46, wrist-worn accelerometers were used to monitor MVPA (≥3.5 METs) during a period of two weeks, and knee radiographs were used to obtain three knee breadth measurements (femoral biepicondylar breadth, mediolateral breadth of femoral condyles, mediolateral breadth of the tibial plateau). The association between MVPA and knee breadth was analyzed using general linear models with adjustments for body mass index, smoking, education years, and accelerometer weartime. Results Of the sample, 54.8% were women. Most individuals were non-smokers (54.6%) and had 9—12 years of education (69.6%). Mean body mass index was 26.2 (standard deviation 4.3) kg/m2. MVPA was uniformly associated with all three knee breadth measurements among both women and men. For each 60 minutes/day of MVPA, the knee breadth dimensions were 1.8—2.0% (or 1.26—1.42 mm) larger among women (p

Abstract Background Modic changes (MC) in the lumbar spine are considered one potential etiological factor behind low back pain (LBP). Multiple risk factors for MC have been suggested, including male gender, smoking and factors affecting hyperloading and mechanical stress such as high body mass index (BMI), strenuous physical work and high occupational and leisure-time physical activity (PA). So far, the effect of PA on the occurrence of MC has remained under debate due to contradictory findings. The purpose of this study was to investigate the possible association between device-measured moderate-to-vigorous PA (MVPA) (≥ 3.5 METs) and lumbar MC. Methods The study had 1374 participants from the Northern Finland Birth Cohort 1966. At the age of 46–48, PA was measured by a wrist-worn accelerometer, and lumbar magnetic resonance imaging (MRI) was carried out to determine MC. We analyzed the association between Type 1 (MC1) and Type 2 (MC2) MC and daily amount of MVPA (min/day) using sex-stratified logistic regression models before and after adjustment for BMI, socioeconomic status, smoking, and accelerometer wear time. Results Among men, increased amount of MVPA was positively associated with any MC (adjusted OR corresponding to every 60 min/day of MVPA 1.41; 95% confidence interval (CI) 1.01 to 1.95) and MC2 (OR 1.54; 95% CI 1.14 to 2.08), but not with MC1 (OR 1.06; 95% CI 0.80 to 1.39). Among women, we only found a positive association between MVPA and MC1 before adjustments (unadjusted OR 1.42; 95% CI 1.06 to 1.92). Conclusion Among men, increased amount of MVPA was associated with increased odds of any MC and particularly MC2. Among women, MVPA was not independently associated with MC.

Abstract Purpose A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = − 16.1, and MVPA: B = − 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = − 3.7). Conclusions Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research.

Abstract Background Given the high global prevalence of physical inactivity, there is a need to design cities that support active modes of transportation. High density diverse neighborhoods with good access networks have been associated with enhanced walking and cycling, but there is a lack of large-scale longitudinal studies utilizing a life course perspective to model residential relocation trajectories. The objectives of the present longitudinal study were to model and visualize residential relocation trajectories between 31 and 46 years of age based on neighborhood density, mixed land use and access networks (DMA), and to assess neighborhood DMA as a predictor of self-reported regular walking and cycling and objectively measured physical activity. Methods Based on data from the Northern Finland Birth Cohort 1966 (N = 5947), we used self-reported regular walking and cycling and objectively measured physical activity as outcome variables and objectively assessed neighborhood DMA as the main explanatory variable. We conducted sequence analysis to model residential relocation trajectories, and generalized linear mixed models and Fisher’s exact test were used to explore longitudinal associations between neighborhood DMA and physical activity. Results Over 80% of the participants lived in a neighborhood with the same level of neighborhood DMA during the follow-up. Relocation occurred more often from higher to lower DMA neighborhoods than reverse. Increased neighborhood DMA was associated with increased regular walking (OR 1.03; 95% CI: 1.00, 1.05; p = 0.023) and cycling (OR 1.17; 95% CI: 1.12, 1.23; p

Background Due to rapid urbanization, there is a need to better understand the relative roles of residential environment and physical activity in depression. We aimed to investigate whether neighbourhood characteristics are related to the presence of depressive symptoms and whether the association is modified by physical activity. Methods This cross-sectional study used the 46-year-old follow-up data (n  =   5489) from the Northern Finland Birth Cohort 1966. Data on depressive symptoms, measured by Beck Depression Inventory-II, and self-reported and accelerometer-measured physical activity were included. Neighbourhood characteristics, population density, distance to the closest grocery store, bus stops and cycle/pedestrian paths, distance to the nearest parks and forests, residential greenness and level of urbanicity were calculated using Geographic Information System methods based on participants' home coordinates. Results According to ordinal logistic regression analyses adjusted for physical activity at different intensities and individual covariates, living in a neighbourhood with higher population density and urbanicity level were associated with a higher risk of experiencing more severe depressive symptoms. Higher residential greenness was associated with a lower risk of experiencing more severe depressive symptoms after adjustment for self-reported light and moderate-to-vigorous physical activity, accelerometer-measured moderate-to-vigorous physical activity and individual covariates. Both higher self-reported and accelerometer-measured physical activity were independently associated with a lower risk of more severe depressive symptoms. Conclusions Both residential environment and physical activity behaviour play an important role in depressive symptoms; however, further research among populations of different ages is required. Our findings can be utilized when designing interventions for the prevention of depression. [ABSTRACT FROM AUTHOR]

Background: Insufficient sleep has been linked to the accumulation of cardiometabolic risks while physical activity acts as a protective factor. Also, sleep regularity may play a critical role in maintaining optimal cardiometabolic health. This cross-sectional study examined the association between device-based sleep regularity, waking activity behaviors, and cardiometabolic health markers, including blood pressure level; abdominal adiposity level; and blood glucose, insulin, and cholesterol. Methods: We included 3698 members of the Northern Finland Birth cohort 1966 who participated in the follow-up study at the age of 46 years between 2012 and 2014 (women 61%). We used seven-day standard deviations of device-based bedtime, wake-up time, and time in bed to reflect sleep regularities. As covariates in linear regression models, we used commonly known potential risk factors in (gender, education, marital status, work schedule, smoking status, alcohol risk use, seven-day time in bed mean, chronotype). In addition to the previous, we used either sedentary time or total physical activity as a covariate (B coefficients with 95% confidence intervals CI). Results: When we considered sedentary time with other covariates, irregularities in bedtime, wake-up time, and time in bed were associated with unfavorable cardiometabolic health markers, such as higher body mass index (bedtime regularity: 0.194, 95% CI [0.072, 0.316], p = 0.002); higher diastolic blood pressure levels (time in bed regularity: 0.175, 95% CI [0.044, 0.306], p = 0.009); and higher 2-h glucose levels (wake-up time regularity: 0.107, 95% CI [0.030, 0.184], p = 0.006). When we considered total physical activity with other covariates, only irregular bedtime was associated with higher waist circumference (B 0.199, 95% CI [0.042, 0.356], p = 0.013). Irregularities in bedtime and wake-up time were not associated with higher diastolic blood pressure, higher visceral fat area or higher fasting insulin level after considering sedentary time or total physical activity with other covariates (in all, p > 0.05). Conclusions: In middle-aged, physical activity appears to weaken the adverse relationship between irregular sleep and cardiometabolic health markers, although the interpretation of the impact of sedentary time remains less conclusive. The clinical significance and extent of the observed associations warrant further investigation. [ABSTRACT FROM AUTHOR]

Aim: The aim of the study was to describe the level, types and determinants of leisure time PA and exercise among children with type 1 diabetes and their parents. Methods: One hundred twenty children aged 6–18 years with type 1 diabetes and 113 parents (n = 113) participated to this questionnaire‐based study at Northern Ostrobothnia District Hospital in Oulu, western Finland. All participants gave informed consent before entering this study. Results: Twenty‐three per cent of the children exercised briskly for at least 7 h a week which corresponds to 60 min per day. The total PA occasions children had with a parent accounted for the children's total number of PA occasions in a week (β = 0.83, 95% CI 0.20–1.47) and total weekly hours of PA (β = 0.90, 95% CI 0.07–1.73). There was a positive association between total weekly hours of brisk PA and HbA1c (β = 0.65, 95% CI 0.02–0.13), while there was no such association with light PA (β = 0.42, 95% CI −0.04–0.87). Laziness, fear of unexpected glycaemic variability and tiredness were the most frequent barriers to PA in children. Conclusion: Most of the children with type 1 diabetes did not reach generally recommended 60 min of brisk PA a day. Exercising with a parent was positively associated with children's weekly frequency and total hours of PA. [ABSTRACT FROM AUTHOR]

Purpose: This population‐based study examines the associations between physical activity (PA), residential environmental greenness, and cardiac health measured by resting short‐term heart rate variability (HRV). Methods: Residential greenness of a birth cohort sample (n = 5433) at 46 years was measured with normalized difference vegetation index (NDVI) by fixing a 1 km buffer around each participant's home. Daily light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and the combination of both (MVPA) were measured using a wrist‐worn accelerometer for 14 days. Resting HRV was measured with a heart rate monitor, and generalized additive modeling (GAM) was used to examine the association between PA, NDVI, and resting HRV. Results: In nongreen areas, men had less PA at all intensity levels compared to men in green areas. Women had more LPA and total PA and less MPA, MVPA, and VPA in green residential areas compared to nongreen areas. In green residential areas, men had more MPA, MVPA, and VPA than women, whereas women had more LPA than men. GAM showed positive linear associations between LPA, MVPA and HRV in all models. Conclusions: Higher LPA and MVPA were significantly associated with increased HRV, irrespective of residential greenness. Greenness was positively associated with PA at all intensity levels in men, whereas in women, a positive association was found for LPA and total PA. A positive relationship of PA with resting HRV and greenness with PA was found. Residential greenness for promoting PA and heart health in adults should be considered in city planning. [ABSTRACT FROM AUTHOR]

Discriminating sleep period from accelerometer data remains a challenge despite many studies have adapted 24-h measurement protocols. We aimed to compare and examine the agreement among device-estimated and self-reported bedtime, wake-up time, and sleep periods in a sample of adults. Participants (108 adults, 61 females) with an average age of 33.1 (SD 0.4) were asked to wear two wearable devices (Polar Active and Ōura ring) simultaneously and record their bedtime and wake up time using a sleep diary. Sleep periods from Polar Active were detected using an in-lab algorithm, which is openly available. Sleep periods from Ōura ring were generated by commercial Ōura system. Scatter plots, Bland–Altman plots, and intraclass correlation coefficients (ICCs) were used to evaluate the agreement between the methods. Intraclass correlation coefficient values were above 0.81 for bedtimes and wake-up times between the three methods. In the estimation of sleep period, ICCs ranged from 0.67 (Polar Active vs. sleep diary) to 0.76 (Polar Active vs. Ōura ring). Average difference between Polar Active and Ōura ring was −1.8 min for bedtimes and −2.6 min for wake-up times. Corresponding values between Polar Active and sleep diary were −5.4 and −18.9 min, and between Ōura ring and sleep diary −3.6 min and −16.2 min, respectively. Results showed a high agreement between Polar Active activity monitor and Ōura ring for sleep period estimation. There was a moderate agreement between self-report and the two devices in estimating bedtime and wake-up time. These findings suggest that potentially wearable devices can be interchangeably used to detect sleep period, but their accuracy remains limited. Estimation of sleep period from different devices could be comparable. Difference between sleep periods from monitors and sleep diary are under 20 min. Device-based estimation of sleep period is encouraged in population-based studies. [ABSTRACT FROM AUTHOR]

This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 h of movement and non‐movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4585 members of the Northern Finland Birth Cohort 1966, who wore a hip‐worn accelerometer at the age of 46 years for 14 consecutive days. Time spent in sedentary behaviors, light‐intensity physical activity (LPA), and moderate‐to‐vigorous‐intensity physical activity (MVPA) were determined from the accelerometer and combined with self‐reported sleep duration to obtain the 24‐h time use. CRF was estimated from the peak heart rate in a submaximal step test. An isotemporal substitution paradigm was used to examine how sedentary time and substituting sedentary time with an equal amount of LPA, MVPA, or sleep were associated with adiposity markers, blood lipid levels, and fasting glucose and insulin. Sedentary time was independently and adversely associated with the markers of cardiometabolic health, even after adjustment for CRF, but not in partition models including LPA, MVPA, sleep, and CRF. Substituting 60, 45, 30, and 15 min/day of sedentary time with LPA or MVPA was associated with 0.2%–13.7% favorable differences in the cardiometabolic health markers after accounting for LPA, MVPA, sleep, CRF, and other confounders. After adjustment for movement and non‐movement behaviors within the 24‐h cycle, reallocating additional time to both LPA and MVPA was beneficially associated with markers of cardiometabolic health in middle‐aged adults regardless of their CRF level. [ABSTRACT FROM AUTHOR]

Low back pain (LBP) is the leading cause of disability worldwide and often associated with lifestyle factors. However, studies further examining the role of these lifestyle factors in non-specific low back pain in comparison with radicular pain are sparse. The aim of this cross sectional study was to investigate how diverse lifestyle factors are associated with LBP. The study population of 3385 middle aged adults with and without low back pain was drawn from a large Birth 1966 Cohort. Outcome measures were steps per day, abdominal obesity, physical activity and endurance of the back muscles. Back static muscular endurance, abdominal obesity and physical activity were measured by means of the Biering–Sørensen test, waist circumference and a wrist worn accelerometer, respectively. Logistic regression analysis was applied to estimate associations of back static muscular endurance, abdominal obesity and accelerometer-measured physical activity with non-specific low back pain and radicular pain. An additional 1000 steps per day were associated with 4% lower odds of having non-specific low back pain. Participants with abdominal obesity had 46% higher odds of having radicular pain, whereas increases of 10 s in back static muscular endurance and 10 min in daily vigorous physical activity were associated with 5% and 7% lower odds of having radicular pain, respectively. In this population-based study, non-specific low back pain and radicular pain were associated with different lifestyle and physical factors at midlife. Non-specific low back pain was associated only with the average daily number of steps, whereas abdominal obesity was the strongest determinant of radicular pain, followed by vigorous physical activity and back static muscular endurance. The findings of this study contribute to better understand the role of lifestyle factors in both non-specific low back pain and radicular pain. Future longitudinal studies are required to explore causality. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) causes a high disease burden. Physical activity (PA) reduces CVD morbidity and mortality. We aimed to determine the relationship between the composition of moderate‐to‐vigorous PA (MVPA), light PA (LPA), sedentary behavior (SB), and sleep during midlife to the incidence of major adverse cardiac events (MACE) and all‐cause mortality at a 7‐year follow‐up. The study population consisted of Northern Finland Birth Cohort 1966 members who participated in the 46‐year follow‐up in 2012 and were free of MACE (N = 4147). Time spent in MVPA, LPA, and SB was determined from accelerometer data. Sleep time was self‐reported. Hospital visits and deaths were obtained from national registers. Participants were followed until December 31, 2019, or first MACE occurrence (acute myocardial infarction, unstable angina pectoris, stroke, hospitalization due to heart failure, or death due to CVD), death from another cause, or censoring. Cox proportional hazards model was used to estimate hazard ratios of MACE incidence and all‐cause mortality. Isotemporal time reallocations were used to demonstrate the dose–response association between time spent in behaviors and outcome. The 24‐h time composition was significantly associated with incident MACE and all‐cause mortality. More time in MVPA relative to other behaviors was associated with a lower risk of events. Isotemporal time reallocations indicated that the greatest risk reduction occurred when MVPA replaced sleep. Higher MVPA associates with a reduced risk of incident MACE and all‐cause mortality after accounting for the 24‐h movement composition and confounders. Regular engagement in MVPA should be encouraged in midlife. [ABSTRACT FROM AUTHOR]

Objective: Up to 70% of women with polycystic ovary syndrome (PCOS) have pre‐obesity or obesity. The aim of this study was to investigate whether women with PCOS have more weight‐loss attempts than women without PCOS, regardless of BMI. Moreover, women's weight perceptions in relation to previous weight‐loss attempts were evaluated. Methods: A population‐based birth cohort study included women with (n = 278) and without PCOS (control individuals, n = 1560) who were examined at ages 31 and 46 years with questionnaires and clinical examinations. Results: Women with PCOS had more weight‐loss attempts compared with control individuals at age 31 (47% vs. 34%, p < 0.001) and 46 years (63% vs. 47%, p < 0.001). At age 46 years, PCOS was associated with multiple weight‐loss attempts in the adjusted model (odds ratio: 1.43 [95% CI: 1.00‐2.03], p = 0.05). The perception of having overweight was more prevalent in those with PCOS, even among participants with normal weight, at age 31 (PCOS 47% vs. control 34%, p = 0.014) and 46 years (PCOS 60% vs. control 39%, p = 0.001). Conclusions: Women with PCOS were more likely to have experienced multiple weight‐loss attempts and a perception of having overweight compared with control individuals, regardless of obesity status. [ABSTRACT FROM AUTHOR]

Previous studies have utilized regression models to investigate the impact of environmental factors on physical activity. However, such approaches are inadequate for data-driven analysis seeking to identify robust associations from the intricate and multi-variable interactions between physical activity and environmental factors. With the emergence of the concept of the exposome, which encompasses the totality of exposures, this paper explores machine learning models for predicting the percentage of physical inactivity in U.S. counties, while considering 28 social-, economic-, and physical-environmental factors. The aim of this study is to address the research gap and gain insight into the complex associations between environmental exposures and physical activity. Five machine learning models were tested, and the performances were compared to select the best classifier for further investigation. This study used data from the Behavioral Risk Factor Surveillance System (BRFSS) of the Centers for Disease Control and Prevention. The mean population of all counties was 102,841, and the mean percentage of population below 18 years was 22.3%. The partial dependence plot analysis indicated that only one feature – bachelor's degree – exhibited a close-to-linear relationship with physical inactivity. Motor-vehicle crash death rate and mean temperature showed nonlinear and non-monotonic relationships with the predicted percentage of physical inactivity. [ABSTRACT FROM AUTHOR]

Background: Articular surface size is traditionally considered to be a relatively stable trait throughout adulthood. Increased joint size reduces bone and cartilage tissue strains. Although physical activity (PA) has a clear association with diaphyseal morphology, the association between PA and articular surface size is yet to be confirmed. This cross-sectional study aimed to clarify the role of moderate-to-vigorous PA (MVPA) in knee morphology in terms of tibiofemoral joint size.Methods: A sample of 1508 individuals from the population-based Northern Finland Birth Cohort 1966 was used. At the age of 46, wrist-worn accelerometers were used to monitor MVPA (≥3.5 METs) during a period of two weeks, and knee radiographs were used to obtain three knee breadth measurements (femoral biepicondylar breadth, mediolateral breadth of femoral condyles, mediolateral breadth of the tibial plateau). The association between MVPA and knee breadth was analyzed using general linear models with adjustments for body mass index, smoking, education years, and accelerometer weartime.Results: Of the sample, 54.8% were women. Most individuals were non-smokers (54.6%) and had 9-12 years of education (69.6%). Mean body mass index was 26.2 (standard deviation 4.3) kg/m2. MVPA was uniformly associated with all three knee breadth measurements among both women and men. For each 60 minutes/day of MVPA, the knee breadth dimensions were 1.8-2.0% (or 1.26-1.42 mm) larger among women (p < 0.001) and 1.4-1.6% (or 1.21-1.28 mm) larger among men (p < 0.001).Conclusions: Higher MVPA is associated with larger tibiofemoral joint size. Our findings indicate that MVPA could potentially increase knee dimensions through similar biomechanical mechanisms it affects diaphyseal morphology, thus offering a potential target in reducing tissue strains and preventing knee problems. Further studies are needed to confirm and investigate the association between articulation area and musculoskeletal health. [ABSTRACT FROM AUTHOR]

Human Energy Expenditure (EE) is a valuable tool for measuring physical activity and its impact on our body in an objective way. To accurately measure the EE, there are methods such as doubly labeled water and direct and indirect calorimetry, but their cost and practical limitations make them suitable only for research and professional sports. This situation, combined with the proliferation of commercial activity monitors, has stimulated the research of EE estimation (EEE) using machine learning on multimodal data from wearable devices. The article provides an overview of existing work in this evolving field, categorizes it, and makes publicly available an EEE dataset. Such a dataset is one of the most valuable resources for the development of the field but is generally not provided by researchers due to the high cost of collection. Finally, the article highlights best practices and promising future direction for designing EEE methods. [ABSTRACT FROM AUTHOR]

This study investigated whether the timing of infant motor development is associated with self‐reported and accelerometer‐measured physical activity (PA) and sedentary time (ST) in midlife. This population‐based study consisted of 4098 people born in 1966 in Northern Finland (NFBC 1966). Data on nine infant motor developmental milestones included making sounds, holding up the head, grabbing objects, turning from back to tummy, sitting without support, standing with support, walking with support, standing without support, and walking without support. At the age of 46, PA at leisure time and sitting time was self‐reported. PA and ST were also measured with a wrist‐worn Polar Active accelerometer that was instructed to be worn on the non‐dominant hand 24 h/d for 14 days. A multiple linear regression analysis was used to analyze the association between infant motor development and PA and ST in midlife. Later infant motor development was weakly associated with higher accelerometer‐measured light PA, but not with moderate‐to‐vigorous PA. Later infant locomotor development was associated with lower accelerometer‐measured ST (β −0.07, p = 0.012) and lower self‐reported sitting time at work (β −0.06, p = 0.004) in women. In conclusion, later infant motor development was associated with higher light PA and lower sedentary time at middle age. PA is a multifactorial behavior influenced by various factors from early childhood to midlife. Further research is required before more general conclusions can be drawn. [ABSTRACT FROM AUTHOR]

Morning, day, or evening chronotypes differ by the circadian timing of alertness and the preferred timing of sleep. It has been suggested that evening chronotype is associated with low physical activity (PA) and high sedentary time (SED). Our aim was to investigate whether such an association is confirmed by objectively measured PA and SED. In 46‐year follow‐up of the Northern Finland Birth Cohort 1966 study, total PA (MET min/day) and SED (min/day) among 5156 participants were determined using wrist‐worn accelerometers for 14 days. We used the shortened Morningness‐Eveningness Questionnaire to define participants' chronotypes. As covariates, we used self‐reported physical strenuousness of work, health, and demographics, and clinical measures. We used adjusted general linear models (B coefficients with 95% confidence intervals, CI) to analyze how chronotype was related to total PA or SED. As compared to evening chronotype, men with day and morning chronotypes had higher total PA volumes (adjusted B 75.2, 95% CI [8.1, 142.4], P =.028, and 98.6, [30.2, 167.1], P =.005). Men with day and morning chronotypes had less SED (−35.8, [−53.8, 17.8], P <.0001, and − 38.6, [−56.9, −20.2], P <.0001). Among women, morning chronotype was associated with higher total PA (57.8, [10.5, 105.0], P =.017), whereas no association between chronotype and SED emerged. Evening chronotype was associated with low objectively measured PA in both sexes and with high SED in men, even after adjustments for established potential confounders. Chronotype should be considered in PA promotion. [ABSTRACT FROM AUTHOR]

Purpose: A data mining approach was applied to establish a multilevel hierarchy predicting physical activity (PA) behavior, and to methodologically identify the correlates of PA behavior. Methods: Cross-sectional data from the population-based Northern Finland Birth Cohort 1966 study, collected in the most recent follow-up at age 46, were used to create a hierarchy using the chi-square automatic interaction detection (CHAID) decision tree technique for predicting PA behavior. PA behavior is defined as active or inactive based on machine-learned activity profiles, which were previously created through a multidimensional (clustering) approach on continuous accelerometer-measured activity intensities in one week. The input variables (predictors) used for decision tree fitting consisted of individual, demographical, psychological, behavioral, environmental, and physical factors. Using generalized linear mixed models, we also analyzed how factors emerging from the model were associated with three PA metrics, including daily time (minutes per day) in sedentary (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA), to assure the relative importance of methodologically identified factors. Results: Of the 4582 participants with valid accelerometer data at the latest follow-up, 2701 and 1881 had active and inactive profiles, respectively. We used a total of 168 factors as input variables to classify these two PA behaviors. Out of these 168 factors, the decision tree selected 36 factors of different domains from which 54 subgroups of participants were formed. The emerging factors from the model explained minutes per day in SED, LPA, and/or MVPA, including body fat percentage (SED: B = 26.5, LPA: B = − 16.1, and MVPA: B = − 11.7), normalized heart rate recovery 60 s after exercise (SED: B = -16.1, LPA: B = 9.9, and MVPA: B = 9.6), average weekday total sitting time (SED: B = 34.1, LPA: B = -25.3, and MVPA: B = -5.8), and extravagance score (SED: B = 6.3 and LPA: B = − 3.7). Conclusions: Using data mining, we established a data-driven model composed of 36 different factors of relative importance from empirical data. This model may be used to identify subgroups for multilevel intervention allocation and design. Additionally, this study methodologically discovered an extensive set of factors that can be a basis for additional hypothesis testing in PA correlates research. [ABSTRACT FROM AUTHOR]